Get more GPU capacity across regions when your AI applications need it

• Seamless and automated bursting of AI and inference workloads
• Dynamic GPU resource selection across data centers and clouds
• Real-time orchestration with intelligent placement based on availability, proximity, and cost

Intro Paragraph

 Elastic GPU Service (EGS) enables organizations to dynamically expand GPU workloads across clusters and clouds, enhancing capacity and availability for AI and inference applications. When additional GPU resources are needed, EGS automatically bursts workloads to the most optimal cloud or data center based on performance, latency, or cost. EGS empowers enterprises to scale their AI operations flexibly for both short-term spikes and long-term growth, while maintaining control policies, multi-tenant access, and compliance with security and regional regulations.

Key Features

1. Cross-Cluster and Cross-Cloud Bursting:
   Automated migration of AI workloads to available GPU resources across any cloud.
2. Real-Time Resource Awareness:
    Continuously monitors GPU availability, latency, and cost across all connected sites 
3. Intelligent Placement and Prioritization:
   Automatically selects the best location based on user-defined policies (cost, performance, proximity).
4. Flexible GPU Compatibility:
   Supports deployment across diverse GPU SKUs (e.g., A100, L4, H100) with minimal model changes.
5. Policy-Controlled Expansion:
   Maintains compliance, access control, and tenant isolation across clouds.

Benefits

1. Seamless Expansion:
   Burst to any cloud, any cluster, without application rewrites.
2. Lower Operational Overhead:
   Fully automated GPU allocation and workload movement.
3. Guaranteed Application Availability:
   Critical AI and inference services remain resilient under heavy load.
4. Cost Optimization: 
   Selects the most cost-effective GPU resources without sacrificing performance.

How it Works

 EGS dynamically monitors GPU inventory across your hybrid and multi-cloud clusters. When demand spikes, EGS automatically triggers a bursting workflow:

1. Workload Trigger:
   AI applications or inference endpoints detect the need for additional GPU capacity. 
2. Inventory Scan:
   EGS checks real-time GPU availability, wait times, and cost across clusters and clouds.
3. Automated Placement:
   EGS intelligently selects the optimal cluster (data center or cloud) for deployment. 
4. Deployment and Orchestration:
   Workloads are seamlessly provisioned and deployed without user intervention. 
5. Continuous Optimization:
   EGS adapts placement dynamically based on real-time conditions to maintain SLAs. 

Conclusion

 Avesha’s Elastic GPU Service (EGS) enables enterprises to seamlessly expand GPU workloads across clusters and clouds when local capacity runs low. EGS automates the process of identifying available GPUs, deploying workloads, and adapting to changing conditions — optimizing cost, performance, and resilience without disrupting operations